The Use of Euclidean Geometric Distance on RGB Color Space for the Classification of Sky and Cloud Patterns

The current work describes the use of multidimensional Euclidean geometric distance (EGD) and Bayesian methods to characterize and classify the sky and cloud patterns present in image pixels. From specific images and using visualization tools, it was noticed that sky and cloud patterns occupy a typical locus on the red-green-blue (RGB) color space. These two patterns were linearly distributed parallel to the RGB cube's main diagonal at distinct distances. A characterization of the cloud and sky patterns EGD was done by supervision to eliminate errors due to outlier patterns in the analysis. The exploratory data analysis of EGD for sky and cloud patterns showed a Gaussian distribution, allowing generalizations based on the central limit theorem. An intensity scale of brightness is proposed from the Euclidean geometric projection (EGP) on the RGB cube's main diagonal. An EGD-based classification method was adapted to be properly compared with existing ones found in related literature, because they restrict the examined color-space domain. Elimination of this limitation was considered a sufficient criterion for a classification system that has resource restrictions. The EGD-adapted results showed a correlation of 97.9% for clouds and 98.4% for sky when compared to established classification methods. It was also observed that EGD was able to classify cloud and sky patterns invariant to their brightness attributes and with reduced variability because of the sun zenith angle changes. In addition, it was observed that Mie scattering could be noticed and eliminated (together with the reflector's dust) as an outlier during the analysis. Although Mie scattering could be classified with additional analysis, this is left as a suggestion for future work.